Trend Scanning¶
Trend Scanning is both a classification and regression labeling technique introduced by Marcos Lopez de Prado in the following lecture slides: Advances in Financial Machine Learning, Lecture 3/10, and again in his text book Machine Learning for Asset Managers.
For some trading algorithms, the researcher may not want to explicitly set a fixed profit / stop loss level, but rather detect overall trend direction and sit in a position until the trend changes. For example, market timing strategy which holds ETFs except during volatile periods. Trend scanning labels are designed to solve this type of problems.
This algorithm is also useful for defining market regimes between downtrend, no-trend, and uptrend.
The idea of trend-scanning labels are to fit multiple regressions from time t to t + L (L is a maximum look-forward window) and select the one which yields maximum t-value for the slope coefficient, for a specific observation.
Tip
- Classification: By taking the sign of t-value for a given observation we can set {-1, 1} labels to define the trends as either downward or upward.
- Classification: By adding a minimum t-value threshold you can generate {-1, 0, 1} labels for downward, no-trend, upward.
- The t-values can be used as sample weights in classification problems.
- Regression: The t-values can be used in a regression setting to determine the magnitude of the trend.
The output of this algorithm is a DataFrame with t1 (time stamp for the farthest observation), t-value, returns for the trend, and bin.